All Outputs (5)

Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study (2018)
Conference Proceeding
Rosli Razak, T. R., Garibaldi, J. M., Wagner, C., Pourabdollah, A., & Soria, D. (2018). Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (IEEE-SSCI 2018) (420-426). https://doi.org/10.1109/SSCI.2018.8628924

In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducin... Read More about Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study.

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems (2018)
Conference Proceeding
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ( 2960-2965). https://doi.org/10.1109/SMC.2018.00503

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced... Read More about Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems.

Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems (2018)
Conference Proceeding
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

Real world environments face a wide range of sources of noise and uncertainty. Thus, the ability to handle various uncertainties, including noise, becomes an indispensable element of automated decision making. Non-Singleton Fuzzy Logic Systems (NSFLS... Read More about Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms (2018)
Conference Proceeding
Agrawal, U., Pinar, A. J., Wagner, C., Havens, T. C., Soria, D., & Garibaldi, J. M. (2018). Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms.

The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations.... Read More about Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms.

Efficient binary fuzzy measure representation and Choquet integral learning (2018)
Conference Proceeding
Islam, M. A., Anderson, D. T., Du, X., Havens, T. C., & Wagner, C. (2018). Efficient binary fuzzy measure representation and Choquet integral learning. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations (115-126). https://doi.org/10.1007/978-3-319-91473-2_10

The Choquet integral (ChI), a parametric function for information aggregation, is parameterized by the fuzzy measure (FM), which has 2N real-valued variables for N inputs. However, the ChI incurs huge storage and computational burden due to its expon... Read More about Efficient binary fuzzy measure representation and Choquet integral learning.